Unsupervised Anomaly Detection for Network Data Streams in Industrial Control Systems

Unsupervised Anomaly Detection for Network Data Streams in Industrial Control Systems

The development and integration of information technology and industrial control networks have expanded the magnitude of new data; detecting anomalies or discovering other valid information from them is of vital importance to the stable operation of industrial control systems. This paper proposes an...

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Bibliographic Details
Main Authors:	Limengwei Liu, Modi Hu, Chaoqun Kang, Xiaoyong Li
Format:	Article
Language:	English
Published:	MDPI AG 2020-02-01
Series:	Information
Subjects:	anomaly detection industrial control systems data streams incremental learning
Online Access:	https://www.mdpi.com/2078-2489/11/2/105

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